
<h1><span class="yiyi-st" id="yiyi-12">numpy.linalg.eigvalsh</span></h1>
        <blockquote>
        <p>原文：<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigvalsh.html">https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigvalsh.html</a></p>
        <p>译者：<a href="https://github.com/wizardforcel">飞龙</a> <a href="http://usyiyi.cn/">UsyiyiCN</a></p>
        <p>校对：（虚位以待）</p>
        </blockquote>
    
<dl class="function">
<dt id="numpy.linalg.eigvalsh"><span class="yiyi-st" id="yiyi-13"> <code class="descclassname">numpy.linalg.</code><code class="descname">eigvalsh</code><span class="sig-paren">(</span><em>a</em>, <em>UPLO=&apos;L&apos;</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/numpy/numpy/blob/v1.11.3/numpy/linalg/linalg.py#L920-L989"><span class="viewcode-link">[source]</span></a></span></dt>
<dd><p><span class="yiyi-st" id="yiyi-14">计算Hermitian或真实对称矩阵的特征值。</span></p>
<p><span class="yiyi-st" id="yiyi-15">与eigh的主要区别：不计算特征向量。</span></p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name"><span class="yiyi-st" id="yiyi-16">参数：</span></th><td class="field-body"><p class="first"><span class="yiyi-st" id="yiyi-17"><strong>a</strong>：（...，M，M）array_like</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-18">要计算其特征值的复数或实数矩阵。</span></p>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-19"><strong>UPLO</strong>：{&apos;L&apos;，&apos;U&apos;}，可选</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-20">与<em class="xref py py-obj">下</em>相同，“L”表示下方，“U”表示上三角形。</span><span class="yiyi-st" id="yiyi-21">已弃用。</span></p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name"><span class="yiyi-st" id="yiyi-22">返回：</span></th><td class="field-body"><p class="first"><span class="yiyi-st" id="yiyi-23"><strong>w</strong>：（...，M，）ndarray</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-24">特征值按升序排列，每个根据其多样性重复。</span></p>
</div></blockquote>
</td>
</tr>
<tr class="field-odd field"><th class="field-name"><span class="yiyi-st" id="yiyi-25">上升：</span></th><td class="field-body"><p class="first"><span class="yiyi-st" id="yiyi-26"><strong>LinAlgError</strong></span></p>
<blockquote class="last">
<div><p><span class="yiyi-st" id="yiyi-27">如果特征值计算不收敛。</span></p>
</div></blockquote>
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</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-28">也可以看看</span></p>
<dl class="last docutils">
<dt><span class="yiyi-st" id="yiyi-29"><a class="reference internal" href="numpy.linalg.eigh.html#numpy.linalg.eigh" title="numpy.linalg.eigh"><code class="xref py py-obj docutils literal"><span class="pre">eigh</span></code></a></span></dt>
<dd><span class="yiyi-st" id="yiyi-30">对称/ Hermitian数组的特征值和特征向量。</span></dd>
<dt><span class="yiyi-st" id="yiyi-31"><a class="reference internal" href="numpy.linalg.eigvals.html#numpy.linalg.eigvals" title="numpy.linalg.eigvals"><code class="xref py py-obj docutils literal"><span class="pre">eigvals</span></code></a></span></dt>
<dd><span class="yiyi-st" id="yiyi-32">一般实数或复数数组的特征值。</span></dd>
<dt><span class="yiyi-st" id="yiyi-33"><a class="reference internal" href="numpy.linalg.eig.html#numpy.linalg.eig" title="numpy.linalg.eig"><code class="xref py py-obj docutils literal"><span class="pre">eig</span></code></a></span></dt>
<dd><span class="yiyi-st" id="yiyi-34">一般实数或复数数组的特征值和右特征向量。</span></dd>
</dl>
</div>
<p class="rubric"><span class="yiyi-st" id="yiyi-35">笔记</span></p>
<div class="versionadded">
<p><span class="yiyi-st" id="yiyi-36"><span class="versionmodified">版本1.8.0中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-37">广播规则适用，有关详细信息，请参阅<code class="xref py py-obj docutils literal"><span class="pre">numpy.linalg</span></code>文档。</span></p>
<p><span class="yiyi-st" id="yiyi-38">特征值使用LAPACK例程_syevd，_heevd来计算</span></p>
<p class="rubric"><span class="yiyi-st" id="yiyi-39">例子</span></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">numpy</span> <span class="k">import</span> <span class="n">linalg</span> <span class="k">as</span> <span class="n">LA</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="n">j</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="n">j</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">LA</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">array([ 0.17157288,  5.82842712])</span>
</pre></div>
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